conventional data

简明释义

通用数据

英英释义

例句

1.Researchers often compare conventional data 常规数据 with new methodologies to validate their findings.

研究人员经常将conventional data 常规数据与新方法进行比较以验证他们的发现。

2.In traditional research, we often rely on conventional data 常规数据 to draw conclusions.

在传统研究中，我们通常依赖于conventional data 常规数据来得出结论。

3.Many companies still prefer conventional data 常规数据 over big data for decision-making.

许多公司仍然更喜欢在决策中使用conventional data 常规数据而不是大数据。

4.The analysis of conventional data 常规数据 helps us understand market trends.

对conventional data 常规数据的分析帮助我们理解市场趋势。

5.Using conventional data 常规数据 can simplify the process of statistical analysis.

使用conventional data 常规数据可以简化统计分析的过程。

作文

In today's rapidly evolving technological landscape, the importance of data cannot be overstated. Among the various types of data, conventional data (传统数据) plays a pivotal role in many industries. This term refers to the structured information that has been collected and analyzed over the years, often through traditional methods such as surveys, experiments, and observational studies. While we now have access to vast amounts of unstructured data generated from social media, sensors, and other digital platforms, conventional data (传统数据) remains crucial for several reasons.Firstly, conventional data (传统数据) is essential for establishing benchmarks and standards. For example, in the field of healthcare, historical patient records provide a baseline for assessing the effectiveness of new treatments. By comparing current outcomes with past data, medical professionals can determine whether a new approach is yielding better results. This reliance on conventional data (传统数据) enables practitioners to make informed decisions based on proven evidence rather than speculation.Secondly, conventional data (传统数据) is often more reliable than unstructured data. The processes used to gather and analyze this type of data are typically rigorous and methodical, which helps ensure accuracy. For instance, in market research, surveys conducted using standardized questionnaires yield consistent and comparable results. In contrast, unstructured data from online reviews or social media posts may be biased or influenced by emotions, making it less dependable for critical business decisions. Therefore, organizations often turn to conventional data (传统数据) when they need trustworthy insights.Moreover, conventional data (传统数据) allows for longitudinal studies, which are invaluable for understanding trends over time. Researchers can track changes in behavior, preferences, or health outcomes across different demographics, providing insights that can inform policy and strategy. For example, educational institutions may analyze student performance data over several years to identify factors that contribute to academic success or failure. Such analyses would not be possible without a solid foundation of conventional data (传统数据).However, it is important to recognize that while conventional data (传统数据) has its strengths, it also has limitations. One major drawback is that it often cannot capture the complexity of human behavior or the nuances of real-world situations. As a result, relying solely on conventional data (传统数据) may lead to oversimplified conclusions. To address this issue, many researchers advocate for a blended approach that incorporates both conventional data (传统数据) and unstructured data. By leveraging the strengths of both types, analysts can gain a more comprehensive understanding of the phenomena they are studying.In conclusion, conventional data (传统数据) remains a cornerstone of research and analysis across various fields. Its reliability, ability to establish benchmarks, and suitability for longitudinal studies make it indispensable. However, it is essential to complement it with other forms of data to capture the full spectrum of insights available. As we continue to navigate an increasingly data-driven world, the integration of conventional data (传统数据) with emerging technologies will undoubtedly enhance our understanding and decision-making capabilities.

在当今快速发展的技术环境中，数据的重要性不容低估。在各种数据类型中，conventional data（传统数据）在许多行业中发挥着关键作用。这个术语指的是通过传统方法（如调查、实验和观察研究）收集和分析的结构化信息，这种信息经过多年的积累。虽然我们现在可以访问从社交媒体、传感器和其他数字平台生成的大量非结构化数据，但conventional data（传统数据）仍然因多种原因而至关重要。首先，conventional data（传统数据）对于建立基准和标准至关重要。例如，在医疗保健领域，历史患者记录提供了评估新治疗效果的基线。通过将当前结果与过去的数据进行比较，医疗专业人员可以确定新的方法是否产生了更好的结果。这种对conventional data（传统数据）的依赖使从业者能够根据经验证据做出明智的决策，而不是基于猜测。其次，conventional data（传统数据）通常比非结构化数据更可靠。收集和分析这种数据所使用的过程通常是严格和系统的，这有助于确保准确性。例如，在市场研究中，使用标准化问卷进行的调查会产生一致且可比较的结果。相比之下，来自在线评论或社交媒体帖子的非结构化数据可能存在偏见或受到情绪的影响，从而使其在关键商业决策中不那么可靠。因此，组织在需要可信见解时通常会转向conventional data（传统数据）。此外，conventional data（传统数据）允许进行纵向研究，这对于理解随时间变化的趋势至关重要。研究人员可以跟踪不同人口统计群体中的行为、偏好或健康结果的变化，提供可以为政策和战略提供信息的见解。例如，教育机构可能会分析几年来学生的表现数据，以识别促成学业成功或失败的因素。如果没有坚实的conventional data（传统数据）基础，这种分析将是不可能的。然而，重要的是要认识到，尽管conventional data（传统数据）有其优势，但它也有局限性。一个主要缺点是，它通常无法捕捉人类行为的复杂性或现实情况的细微差别。因此，仅依赖conventional data（传统数据）可能导致过于简单化的结论。为了解决这一问题，许多研究人员提倡采用一种混合方法，结合conventional data（传统数据）和非结构化数据。通过利用这两种类型的优势，分析师可以获得对他们研究现象的更全面理解。总之，conventional data（传统数据）仍然是各个领域研究和分析的基石。它的可靠性、建立基准的能力以及适合进行纵向研究的特点使其不可或缺。然而，重要的是要与其他形式的数据相结合，以捕捉可用的全方位见解。随着我们继续在一个日益以数据驱动的世界中导航，conventional data（传统数据）与新兴技术的整合无疑将增强我们的理解和决策能力。